Neural networks have been found to be highly effective and widely applicable to a large number of audio and speech problems, including speech enhancement, where the goal is to improve the quality and intelligibility of degraded speech signals. In recent years, several neural architectures have reported state-of-the-art results for supervised [1]}, real-time [2]} and semi-supervised [3]} speech enhancement tasks. Mostly driven by supervised learning, training such models requires large amounts of audio data which are expected to closely match the distribution of the test time input noisy recordings. While limited supervised data might often be available, supervised speech enhancement systems trained on them provides severely inferior performance due to the mismatch with the actual test distribution.
